Open Research Directions in Quantum Principal Component Analysis (QPCA)

Résumé fourni par la source

Quantum Principal Component Analysis (QPCA) is rapidly becoming an integral part of quantum machine learning (QML) because it will give an important acceleration over traditional PCA for assessing widespread quantum information. In terms of how QPCA works, it will leverage quantum parallelism to extract the primary eigenvectors and eigenvalues from the quantum states. After isolating the dominant eigenvalues and eigenvectors, techniques for data compression, dimensionality reduction, and pattern recognition can become operational in a quantum computing (QC) platform. However, we will still have to face what quantum noise represents robustly; the coherence time limits of the qubits and the concerns associated with collecting and preparing quantum states before the approach of the process described above can be a reality. This chapter has examined the field of QPCA as it stands today, discussed limitations in existing methods and algorithms for QPCA, and identified possible future avenues of research that include error-resilient quantum circuits, hybrid quantum/classical PCA models, and scaling to near-term quantum devices. It has also examined how QPCA may contribute to quantum-enhanced data science and the future of more general quantum technologies. To value researchers and push the boundaries of both the theoretical and practical horizons of QPCA forward, we have carefully conducted a review of the current state, remaining challenges, and possible future directions of QPCA.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.